Wei Y, Forelli R F, Hansen C, Levesque J P, Tran N, Agar J C, Di Guglielmo G, Mauel M E, Navratil G A
Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA.
Real-time Processing Systems Division, Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA.
Rev Sci Instrum. 2024 Jul 1;95(7). doi: 10.1063/5.0190354.
Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. In this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.
在磁约束聚变装置中，主动反馈控制有助于减轻等离子体不稳定性并实现稳健运行。光学高速摄像机提供了一种强大的非侵入性诊断工具，适用于这些应用。在本研究中，我们在现场可编程门阵列（FPGA）硬件上以超过100 kfps的速率处理高速摄像机数据，以跟踪磁流体动力学（MHD）模式的演变并实时生成控制信号。我们的系统利用卷积神经网络（CNN）模型，该模型使用摄像机图像预测n = 1 MHD模式的幅度和相位，其精度高于其他经过测试的非深度学习方法。通过在高速摄像机诊断的标准FPGA读出硬件中直接实现此模型，我们的模式跟踪系统实现了17.6 μs的总触发到输出延迟和高达120 kfps的吞吐量。在高β托卡马克扩展脉冲（HBT-EP）实验中的这项研究展示了一种基于FPGA的高速摄像机数据采集和处理系统，可应用于基于实时机器学习的托卡马克诊断和控制以及其他科学领域的潜在应用。
